Bridging Graph Drawing and Dimensionality Reduction with Stochastic Stress Optimization
Both Dimensionality Reduction (DR) and Graph Drawing (GD) aim to visualize abstract, non-linear structures, yet rely on different optimization paradigms. This contrast is evident in Multidimensional Scaling (MDS), which typically depends on the SMACOF algorithm despite graph drawing results showing that simpler stochastic optimization schemes can be more effective for the same objective. We bridge these domains by adapting Stochastic Gradient Descent (SGD) techniques from graph drawing to vector data embedding. We present a scikit-learn compatible estimator that minimizes global stress through local pairwise updates, improving upon the existing implementation. Experiments on standard high...
| Authors | Daniel Hangan, Stephen Kobourov, Jacob Miller |
|---|---|
| Primary category | cs.LG |
| Published | 1 May 2026 |
| Last updated at source | 1 May 2026 |
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